Engineering The Future Ai Augmented Devsecops And Cloud Native Platforms For The Enterprise 2025

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Engineering The Future Ai Augmented Devsecops And Cloud Native Platforms For The Enterprise 2025

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Author : Author:1-Chandrakanth Devarakadra Anantha, Author:2-Dr Priyanka Kaushik
language : en
Publisher: RAVEENA PRAKASHAN OPC PVT LTD
Release Date :

Engineering The Future Ai Augmented Devsecops And Cloud Native Platforms For The Enterprise 2025 written by Author:1-Chandrakanth Devarakadra Anantha, Author:2-Dr Priyanka Kaushik and has been published by RAVEENA PRAKASHAN OPC PVT LTD this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE The rapid evolution of technology has fundamentally altered how enterprises operate, with a significant shift towards cloud-native platforms and AI-powered tools. The convergence of artificial intelligence (AI) and DevSecOps (Development, Security, and Operations) has brought about a new era in enterprise technology, one that emphasizes automation, scalability, and security in every layer of the development lifecycle. “Engineering the Future: AI-Augmented DevSecOps and Cloud-Native Platforms for the Enterprise” explores this transformative intersection, offering a comprehensive guide to understanding and leveraging AI and cloud-native technologies to drive innovation, efficiency, and security within the enterprise ecosystem. At its core, this book delves into how AI can augment DevSecOps practices to foster a more secure, agile, and efficient development pipeline. By integrating AI into the DevSecOps process, organizations can achieve enhanced automation, proactive threat detection, and real-time insights, making it easier to develop and deploy secure applications in increasingly complex cloud environments. AI-powered solutions can detect vulnerabilities, optimize workflows, and automate compliance checks, allowing development teams to focus on innovation without sacrificing security. As businesses embrace cloud-native architectures, where microservices and containerization enable greater flexibility and scalability, the need for AI to facilitate seamless operations across distributed systems becomes ever more critical. The enterprise landscape has witnessed an unprecedented shift towards cloud-first strategies, which have revolutionized the way applications are developed, deployed, and maintained. Cloud-native platforms enable enterprises to accelerate their digital transformation, providing the agility to rapidly scale and innovate while ensuring robust security measures are embedded into every stage of the development lifecycle. Cloud-native technologies, such as Kubernetes, containerization, and serverless architectures, have become essential building blocks for modern enterprise applications. However, with this new paradigm come complex challenges in managing infrastructure, maintaining security, and ensuring smooth integration across diverse environments. This book offers insights into how AI-augmented DevSecOps practices can address these challenges, enabling organizations to stay ahead in an increasingly competitive and fast-paced business world. The synergy between AI and cloud-native platforms is particularly evident in the areas of continuous integration and continuous delivery (CI/CD), where AI-driven tools can enhance deployment efficiency and reduce human errors. By automating repetitive tasks, AI-powered systems free up valuable developer time, allowing them to focus on higher-value activities that directly contribute to business growth. Furthermore, AI’s predictive capabilities enable proactive decision-making, identifying potential bottlenecks, vulnerabilities, or failures before they affect production environments. This is especially important as enterprises adopt multi-cloud and hybrid cloud strategies, where seamless integration, monitoring, and security across various cloud platforms are critical to maintaining operational continuity. Security is at the forefront of every conversation in the world of DevSecOps, particularly as cyber threats become more sophisticated and persistent. AI plays a vital role in strengthening security frameworks by automating threat detection, identifying abnormal patterns, and responding to incidents in real-time. The integration of AI into security processes within DevSecOps workflows helps organizations address vulnerabilities faster and more efficiently, reducing the window of opportunity for attackers. This book examines how AI can enhance traditional security measures, enabling organizations to secure their cloud-native applications against ever-evolving threats. As enterprises continue to evolve in the digital age, the role of AI in augmenting DevSecOps and cloud-native platforms will only grow more pivotal. Organizations that embrace these technologies will be better positioned to innovate at scale while ensuring their applications remain secure and resilient. This book is designed for IT leaders, product managers, developers, and security professionals who are seeking to navigate the complexities of AI, DevSecOps, and cloud-native technologies. Whether you are looking to integrate AI into your DevSecOps pipeline, adopt cloud-native architectures, or enhance your enterprise’s security posture, “Engineering the Future” provides the necessary tools, frameworks, and strategies to succeed in this rapidly evolving landscape. In the pages that follow, you will gain a deeper understanding of how AI can drive automation and intelligence in DevSecOps practices, how cloud-native platforms are transforming enterprise IT operations, and how organizations can seamlessly integrate these technologies to build the secure, scalable, and agile applications of tomorrow. Welcome to the future of enterprise technology—one where AI and cloud-native platforms work hand in hand to drive innovation, security, and operational excellence. Authors

Ai Driven Enterprise Architecture From Data Engineering To Generative Ai 2025

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Author : Author:1- Bhanuvardhan Nune, Author:2-Dr. Gaurav Kumar
language : en
Publisher: RAVEENA PRAKASHAN OPC PVT LTD
Release Date :

Ai Driven Enterprise Architecture From Data Engineering To Generative Ai 2025 written by Author:1- Bhanuvardhan Nune, Author:2-Dr. Gaurav Kumar and has been published by RAVEENA PRAKASHAN OPC PVT LTD this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE In the rapidly evolving landscape of technology, enterprises are increasingly turning to artificial intelligence (AI) to drive innovation, efficiency, and growth. The integration of AI into enterprise architecture has shifted from a trend to an essential strategy for businesses looking to maintain a competitive edge. AI-Driven Enterprise Architecture: From Data Engineering to Generative AI is written to explore the transformative impact of AI across all layers of enterprise systems, from data engineering and analytics to innovative generative AI technologies that are reshaping industries. In today’s digital age, businesses face an explosion of data that is often unstructured, decentralized, and sold. For AI to truly revolutionize enterprise systems, there must be a solid architecture that not only supports large-scale data processing but also enables the seamless integration of AI technologies into every corner of the organization. This book takes a comprehensive approach to AI-driven enterprise architecture, focusing on the technical, strategic, and operational challenges and opportunities associated with AI adoption. The journey from data engineering to generative AI requires a solid foundation of data management and processing capabilities. The book begins by discussing the critical importance of data engineering, the practice of building robust systems for collecting, storing, and transforming data into actionable insights. Understanding how to build and maintain efficient data pipelines, databases, and data lakes forms the backbone of AI integration in an enterprise. This foundational understanding sets the stage for deploying machine learning (ML) models and AI-driven tools, which require sophisticated infrastructure to function on a scale. The integration of machine learning and AI models into enterprise architecture is the central focus of this book. As businesses recognize the value of AI in improving decision-making, automation, and customer experiences, this book guides readers through how to implement AI across multiple enterprise functions. From predictive analytics and automation to natural language processing (NLP) and computer vision, we will examine how these AI technologies interact with existing enterprise systems to create smarter, more efficient business operations. One of the most exciting and rapidly advancing fields in AI is generative AI—a technology that can create new data, designs, or content based on learned patterns. Generative AI tools like GPT-3, DALL-E, and stable diffusion models are now being used to generate text, images, code, and even video. The power of these models lies in their ability to produce new, high-quality content that can be harnessed for marketing, customer engagement, product development, and innovation. This book explores how generative AI fits within the broader enterprise architecture and how businesses can leverage these capabilities to unlock new value streams, foster creativity, and enhance productivity. AI-Driven Enterprise Architecture: From Data Engineering to Generative AI is designed for business leaders, data engineers, architects, and AI practitioners who are looking to understand the potential of AI in their organizations. Through real-world case studies, best practices, and technical insights, this book aims to provide a holistic view of how AI-driven enterprise architecture can deliver long-term strategic value. The book also delves into the challenges and ethical considerations of AI implementation, particularly with regard to data privacy, algorithmic bias, and governance, ensuring that AI is deployed responsibly and sustainably. As businesses embrace AI technologies, it is clear that the future of enterprise architecture will be driven by data-centric, AI-powered models that allow organizations to be more adaptive, responsive, and innovative. This book offers a roadmap for navigating that future, helping organizations transform their architecture to support the AI-driven, intelligent enterprise of tomorrow. We invite you to embark on this journey through the evolving world of AI-driven enterprise architecture, where the combination of data engineering, machine learning, and generative AI is shaping the future of businesses across the globe. Authors

Enterprise Reinvented Ai Cloud And Data At Scale 2025

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Author : Author:1- Souvari Ranjan Biswal, Author:2-Dr. Nagaraj S
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
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Enterprise Reinvented Ai Cloud And Data At Scale 2025 written by Author:1- Souvari Ranjan Biswal, Author:2-Dr. Nagaraj S and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE In an era defined by digital disruption, enterprises face a singular imperative: to harness the synergistic power of artificial intelligence, cloud computing, and data at unprecedented scale. “Enterprise Reinvented: AI, Cloud, and Data at Scale” emerges from this landscape as both a strategic manifesto and a practical playbook, guiding leaders, architects, and technologists through the seismic shift from monolithic legacy systems to adaptive, intelligence-driven platforms. Rather than viewing AI, cloud, and data as discrete initiatives, this book treats them as deeply intertwined pillars of business reinvention—each amplifying the others to unlock agility, resilience, and transformative insight. We begin by exploring the tectonic forces reshaping the modern enterprise: the exponential growth of data volumes, the maturation of containerized and serverless cloud architectures, and the democratization of machine learning through open-source frameworks and managed services. In these opening chapters, you will discover how strategic alignment between data governance, platform engineering, and AI-driven innovation sets the stage for truly scalable outcomes—from real-time customer personalization and predictive maintenance to autonomous supply chains and intelligent risk management. Subsequent sections dive into the pragmatic mechanics of building “AI-ready” cloud platforms: designing data fabrics that ensure quality, lineage, and compliance; implementing cloud-native architectures that support burst-to-edge workloads; and establishing ML Ops pipelines for continuous model training, validation, and deployment. Case studies drawn from industries as diverse as manufacturing, financial services, and healthcare illustrate how leading organizations navigate governance, security, and cost-optimization challenges while accelerating time-to-value for analytic and AI use cases. Finally, the book offers a forward-looking perspective on the next frontier: how emerging paradigms—such as distributed AI at the edge, digital twins of business processes, and federated learning ecosystems—will redefine the contours of enterprise scale. We also examine the organizational and cultural shifts required to sustain this transformation: cross-functional “platform teams,” data-literate leadership, and an experimentation mindset that balances rigorous risk management with audacious, data-driven ambition. Authors

Ai Native Digital Platforms Architecting The Next Wave Of Commerce And Intelligence 2025

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Author : Author:1-Amit Ojha, Author:2-Mr. Kumar Pal Singh
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
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Ai Native Digital Platforms Architecting The Next Wave Of Commerce And Intelligence 2025 written by Author:1-Amit Ojha, Author:2-Mr. Kumar Pal Singh and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE In an age where artificial intelligence is no longer a futuristic concept but a fundamental driver of digital transformation, the convergence of AI and platform architecture is redefining how we innovate, interact, and transact. The rise of AI-native digital platforms marks a profound shift in technological design, one that transcends traditional boundaries and paves the way for adaptive, intelligent, and autonomous systems capable of responding to evolving market demands in real time. This book, AI-Native Digital Platforms: Architecting the Next Wave of Commerce and Intelligence, is an exploration of the emerging paradigm in which artificial intelligence is embedded at the very core of digital platform design. It aims to offer a comprehensive framework for understanding how AI reshapes platform architecture—across data pipelines, microservices, edge computing, cloud orchestration, API ecosystems, and user experience personalization. From dynamic supply chains to intelligent customer engagement, AI-native platforms are becoming the backbone of future-ready enterprises. We begin with foundational concepts of digital platforms and AI technologies, then progress through architectural principles, design patterns, case studies, and strategic implementation models. The book addresses key challenges such as ethical AI governance, scalability, interoperability, security, and data sovereignty, all while emphasizing real-world applications across e-commerce, fintech, healthcare, smart cities, and industrial automation. Intending for technology leaders, architects, data scientists, product strategists, and academic researchers, this book bridges the gap between theoretical AI capabilities and practical platform engineering. It reflects a multi-disciplinary perspective shaped by innovations in machine learning, distributed systems, human-centered design, and business intelligence. As AI-native platforms become central to global digital economies, the decisions we make today will influence the societal, ethical, and economic fabric of tomorrow. This book is a guide and a call to action—for building platforms that are not only intelligent but also inclusive, sustainable, and resilient. Authors

Ai Driven Networks Architecting The Future Of Autonomous Secure And Cloud Native Connectivity 2025

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Author : AUTHOR:1-DIPESH JAGDISH KASHIV, AUTHOR:2-PROF (DR) MOPARTHI NAGESWARA RAO
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
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Ai Driven Networks Architecting The Future Of Autonomous Secure And Cloud Native Connectivity 2025 written by AUTHOR:1-DIPESH JAGDISH KASHIV, AUTHOR:2-PROF (DR) MOPARTHI NAGESWARA RAO and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE In an age defined by relentless digital innovation, networks have evolved far beyond simple conduits for data. They now serve as the critical nervous system of entire industries—powering everything from real-time financial transactions to massive Internet-of-Things deployments and immersive 5G applications. Yet the exponential growth in traffic volumes, the dynamic nature of modern applications, and the sophistication of cyber-threats have exposed the limitations of static, manually managed infrastructures. AI-Driven Networks: Architecting the Future of Autonomous, Secure, and Cloud-Native Connectivity was conceived to meet this challenge head-on, providing a comprehensive roadmap for embedding intelligence, resilience, and automation into every layer of the network stack. Our journey begins in Chapter 1: Foundations of AI-Driven Networking, where we introduce the core principles that underpin the fusion of artificial intelligence and networking. After grounding readers in key machine-learning paradigms—supervised, unsupervised, and reinforcement learning—we map these techniques onto fundamental networking functions such as routing, traffic classification, and anomaly detection. Building on these fundamentals, Chapter 2: Intent-Based and Self-Driving Architectures explores how high-level business objectives can be translated into automated network behaviors. By examining intent interfaces—ranging from declarative APIs to natural-language processing tools—we demonstrate how directives like “ensure sub-5 ms latency between our core data centers” can be codified, deployed, and continuously enforced across software-defined networking controllers, routers, and security gateways. In Chapter 3: Data-Plane Intelligence—From Telemetry to Insights, we dive into the lifeblood of AI-driven networks: data. Modern network devices emit rich, high-velocity telemetry streams—flow records, per-queue latency histograms, packet-level metrics—and ingesting, storing, and analyzing this data at scale is a monumental engineering challenge. We detail scalable architectures for real-time telemetry collection, explore unsupervised anomaly-detection models that surface emerging congestion hotspots, and show how predictive analytics can forecast capacity needs hours or days in advance to enable proactive resource scaling. Chapter 4: Control-Plane Optimization with Reinforcement introduces reinforcement learning as the engine for adaptive, closed-loop control. Beginning with tabular Q-Learning methods that dynamically tune link weights in OSPF and segment-routing protocols, we progress to advanced policy-gradient algorithms—REINFORCE and actor-critic variants—that learn to split flows optimally for throughput and fairness. Multi-agent RL scenarios illustrate how multiple controllers, or administrative domains can cooperate or compete to maximize global efficiency, all while honoring strict service-level agreements. Security is woven throughout every chapter, but Chapter 5: Secure by Design—AI for Threat Detection and Response provides an in-depth exploration of zero-trust enforcement and AI-driven defenses. We unpack the “never trust, always verify” paradigm, showing how continuous authentication—powered by behavioral profiling, device-fingerprinting, and contextual risk scoring—can prevent unauthorized lateral movement even after perimeter breaches. AI-based micro-segmentation adapts dynamically to traffic patterns, while deep-learning models detect novel attack vectors. We conclude with frameworks for automated incident response, orchestrating containment actions like host isolation, firewall rule updates, and credential rotations in real time. As networks embrace containerization and cloud-native platforms, Chapter 6: Cloud-Native and Kubernetes Integration examines how microservices design patterns, service meshes, and GitOps workflows can host AI inference engines for fine-grained policy enforcement. We show how Kubernetes CNI plugins incorporate ML models for per-pod traffic classification, how canary deployments can be orchestrated through AI-driven traffic-splitting strategies, and how declarative pipelines ensure safe, auditable policy roll-outs. Subsequent chapters synthesize these advancements into end-to-end automation and observability frameworks (Chapters 7–9), explore the unique opportunities at the network edge and in 5G environments (Chapter 10), peer into the future with quantum networking and post-quantum resilience strategies (Chapter 11), and address the governance, compliance, and ethical considerations that accompany the adoption of autonomous, AI-driven networks (Chapter 12). Whether you are a network architect designing carrier-grade backbones, a security engineer safeguarding mission-critical infrastructure, or a researcher advancing autonomous systems, this book equips you with the theories, tools, and real-world techniques needed to build networks that not only meet today’s demands but also learn, adapt, and scale as the digital landscape evolves. The future of connectivity is intelligent—and it starts here. Authors Dipesh Jagdish Kashiv

Artificial Intelligence For Cloud Native Software Engineering

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Author : Chelliah, Pethuru Raj
language : en
Publisher: IGI Global
Release Date : 2025-05-07

Artificial Intelligence For Cloud Native Software Engineering written by Chelliah, Pethuru Raj and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-07 with Computers categories.

Artificial intelligence is transforming software engineering by automating development, testing, deployment, and security processes, leading to more efficient and high-quality software solutions. AI-powered tools enhance scalability, reliability, and real-time analytics, enabling businesses to optimize operations and improve decision-making. As cloud-native architectures gain traction, AI-driven innovations are reshaping the way software is designed, maintained, and evolved, driving a new era of intelligent and adaptive technology solutions. Artificial Intelligence for Cloud-Native Software Engineering explores the transformative impact of AI on the software engineering lifecycle, highlighting its role in automating and enhancing various stages of software development. It provides a comprehensive overview of how AI technologies can assist software architects and engineers in creating high-quality, enterprise-grade software efficiently. Covering topics such as source code creation, data security, and multiparameter optimization, this book is an excellent resource for software engineers, computer scientists, professionals, researchers, scholars, academicians, and more.

Ai Enhanced Observability Intelligent Performance Monitoring For Cloud Native Architectures 2025

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Author : Ankush Jitendrakumar Tyagi, Dr. Lalit Kumar
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Ai Enhanced Observability Intelligent Performance Monitoring For Cloud Native Architectures 2025 written by Ankush Jitendrakumar Tyagi, Dr. Lalit Kumar and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE The rapid evolution of cloud-native architecture has reshaped the way applications are designed, developed, and deployed. With the increasing complexity of these systems, traditional methods of performance monitoring and observability have struggled to keep pace. The need for real-time insights, proactive issue detection, and intelligent decision-making has never been more urgent. In this context, AI-enhanced observability emerges as a critical innovation, enabling businesses to leverage artificial intelligence (AI) and machine learning (ML) to transform how they monitor, analyze, and optimize cloud-native applications. The concept of observability is no longer limited to tracking basic metrics such as CPU usage or memory consumption. Instead, it has expanded to include deeper insights into the overall system behavior, user experiences, and distributed systems’ performance. As cloud-native architectures, powered by technologies like microservices, containers, and serverless computing, become more widespread, monitoring and observing every aspect of a system’s performance has become a highly complex and resource-intensive challenge. AI-enhanced observability addresses this complexity by automating and improving the collection, analysis, and interpretation of vast amounts of data generated by cloud-native applications. This book, AI-Enhanced Observability: Intelligent Performance Monitoring for Cloud-Native Architectures, explores the intersection of AI-driven observability and cloud-native systems. It aims to provide readers with an in-depth understanding of how artificial intelligence and machine learning can be harnessed to deliver smarter performance monitoring, detect anomalies faster, and enable better decision-making in cloud-native environments. Through intelligent monitoring and predictive insights, organizations can move from a reactive approach to a proactive one, identifying issues before they affect performance and ultimately improving the reliability, scalability, and efficiency of their systems. The evolution of cloud-native architecture has led to the proliferation of diverse and distributed components, often running in dynamic and highly elastic environments. Traditional tools, which were originally designed for more static, monolithic systems, can no longer handle the volume, velocity, and variety of data required to gain comprehensive visibility into these modern architectures. AI and machine learning technologies offer the promise of transforming observability from a collection of data points into a comprehensive, intelligent system capable of continuously learning from its environment and delivering actionable insights in real-time. This book covers a range of critical topics, including automated anomaly detection, root cause analysis, predictive monitoring, and adaptive alerting, among others. Each of these concepts plays a crucial role in helping organizations monitor the health of their cloud-native applications and infrastructure. The integration of AI allows for the identification of patterns and behaviors that traditional methods may miss, providing more granular insights into system performance and user experience. As cloud-native architecture continues to grow in complexity, leveraging AI to enhance observability will become not just a best practice but a necessity for maintaining the performance and reliability of modern systems. This book is written for cloud architects, site reliability engineers (SREs), DevOps teams, and anyone involved in the development, deployment, and maintenance of cloud-native applications. Whether you are looking to enhance your organization’s ability to monitor performance, identify bottlenecks, or gain predictive insights into your cloud infrastructure, this book will provide valuable insights and actionable strategies to achieve smarter, more efficient observability. The chapters of this book are organized to introduce the fundamental principles of AI-enhanced observability, followed by detailed discussions on how these concepts are applied to real-world scenarios in cloud-native environments. Each chapter is designed to build upon the previous one, with practical examples, case studies, and step-by-step guides to help readers implement AI-driven observability solutions in their own organizations. In addition to exploring the theoretical underpinnings of AI-enhanced observability, this book also provides practical guidance on selecting the right tools, integrating machine learning models into observability platforms, and addressing the challenges that arise when scaling observability practices in large, complex systems. By the end of this book, readers will have a clear understanding of how AI can be leveraged to improve performance monitoring and observability in cloud-native environments, leading to enhanced operational efficiency, reliability, and user satisfaction. I hope that this book provides you with the knowledge and tools to embrace the future of observability, enabling you to stay ahead of challenges, drive innovation, and optimize the performance of your cloud-native applications. Authors

Cloud Native Architecture Cna And Artificial Intelligence Ai For The Future Of Software Engineering The Principles Patterns Platforms And Practices

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Author :
language : en
Publisher: Academic Press
Release Date : 2026-03-01

Cloud Native Architecture Cna And Artificial Intelligence Ai For The Future Of Software Engineering The Principles Patterns Platforms And Practices written by and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-03-01 with Computers categories.

Cloud-native Architecture (CNA) and Artificial Intelligence (AI) for the Future of Software Engineering: The Principles, Patterns, Platforms and Practices

Revolutionizing Enterprise Systems From Legacy To Cloud A Journey Through Automation Ai And Scalable Solutions

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Author : Sandeep Chinamanagonda
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2024-05-15

Revolutionizing Enterprise Systems From Legacy To Cloud A Journey Through Automation Ai And Scalable Solutions written by Sandeep Chinamanagonda and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-15 with Computers categories.

In an era where digital transformation defines business success, Revolutionizing Enterprise Systems: From Legacy to Cloud offers a comprehensive roadmap for enterprises seeking to modernize their IT infrastructure. This book delves into the challenges of legacy systems, the power of cloud-native architectures, and the game-changing impact of automation and AI-driven enterprise solutions. Explore cutting-edge topics such as DevSecOps for secure IT operations, AI-powered decision-making, and real-time data streaming for enhanced business intelligence. Uncover strategies for overcoming resistance to change, optimizing cloud costs, and leveraging advanced technologies like quantum computing for future-proof enterprise systems. With expert insights from seasoned technology strategist Sandeep Chinamanagonda, this book provides IT leaders, CIOs, and digital transformation professionals with practical strategies to navigate enterprise cloud migration, build resilient architectures, and harness the full potential of AI, automation, and scalable cloud solutions. Whether you’re modernizing legacy systems or preparing for the next wave of enterprise IT evolution, Revolutionizing Enterprise Systems is your essential guide to thriving in a rapidly changing digital landscape.

Scalable Artificial Intelligence Systems Cloud Native Edge Ai Mlops And Governance For Real World Deployment

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Author : Swarup Panda
language : en
Publisher: Deep Science Publishing
Release Date : 2025-07-28

Scalable Artificial Intelligence Systems Cloud Native Edge Ai Mlops And Governance For Real World Deployment written by Swarup Panda and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-28 with Computers categories.

Artificial Intelligence (AI) has become essential across industries, transforming operations, decision-making, and value creation. As organizations worldwide use AI to address challenges in areas like healthcare, finance, cybersecurity, manufacturing, and infrastructure, the need for reliable and scalable AI systems continues to grow. This book offers practical guidance for professionals designing and deploying scalable, compliant AI solutions in production environments. It covers modernizing legacy systems, building MLOps pipelines, and addressing ethical aspects of autonomous AI, providing essential insights and patterns for real-world applications. We cover essential topics for enterprise AI success, such as scalable architectures (cloud-native, edge, hybrid), MLOps for lifecycle management, and governance for compliance and fairness. The text also outlines frameworks for explainable and federated AI in regulated fields, supporting privacy and distributed intelligence. We demonstrate AI's impact on diagnostics, fraud detection, threat intelligence, and urban planning through case studies, and review how platforms like Azure, AWS, and GCP support scalable AI deployment. This book highlights the need for ethical AI that upholds human values, privacy, and transparency. As AI shapes society, we must design, deploy, and govern it responsibly. I invite you to explore these chapters with a mindset of both innovation and accountability—as together, we shape a future powered by intelligent and responsible systems.